Choosing a Pipeline

Choosing an NLU pipeline allows you to customize your model and finetune it on your dataset.

The Short Answer

If your training data is in english, a good starting point is using pretrained_embeddings_convert pipeline.

language: "en"

pipeline: "pretrained_embeddings_convert"

In case your training data is multi-lingual and is rich with domain specific vocabulary, use the supervised_embeddings pipeline:

language: "en"

pipeline: "supervised_embeddings"

A Longer Answer

The three most important pipelines are supervised_embeddings, pretrained_embeddings_convert and pretrained_embeddings_spacy. The pretrained_embeddings_spacy pipeline uses pre-trained word vectors from either GloVe or fastText, whereas pretrained_embeddings_convert uses a pretrained sentence encoding model ConveRT to extract vector representations of complete user utterance as a whole. On the other hand, the supervised_embeddings pipeline doesn’t use any pre-trained word vectors or sentence vectors, but instead fits these specifically for your dataset.

Note

These recommendations are highly dependent on your dataset and hence approximate. We suggest experimenting with different pipelines to train the best model.

pretrained_embeddings_spacy

The advantage of pretrained_embeddings_spacy pipeline is that if you have a training example like: "I want to buy apples", and Rasa is asked to predict the intent for "get pears", your model already knows that the words "apples" and "pears" are very similar. This is especially useful if you don’t have large enough training data.

pretrained_embeddings_convert

Warning
Since ConveRT model is trained only on an English corpus of conversations, this pipeline should only be used if your training data is in English language.

This pipeline uses ConveRT model to extract vector representation of a sentence and feeds them to EmbeddingIntentClassifier for intent classification. The advantage of using pretrained_embeddings_convert pipeline is that it doesn’t treat each word of the user message independently, but creates a contextual vector representation for the complete sentence.

supervised_embeddings

The advantage of the supervised_embeddings pipeline is that your word vectors will be customised for your domain. For example, in general English, the word “balance” is closely related to “symmetry”, but very different to the word “cash”. In a banking domain, “balance” and “cash” are closely related and you’d like your model to capture that. This pipeline doesn’t use a language-specific model, so it will work with any language that you can tokenize (on whitespace or using a custom tokenizer).

MITIE

You can also use MITIE as a source of word vectors in your pipeline. The MITIE backend performs well for small datasets, but training can take very long if you have more than a couple of hundred examples. However, we do not recommend that you use it as MITIE support is likely to be deprecated in a future release.

Comparing different pipelines for your data

Rasa gives you the tools to compare the performance of both of these pipelines on your data directly.

Class imbalance

Classification algorithms often do not perform well if there is a large class imbalance, for example if you have a lot of training data for some intents and very little training data for others. To mitigate this problem, rasa’s supervised_embeddings pipeline uses a balanced batching strategy.

language: "en"

pipeline:
- name: "CountVectorsFeaturizer"
- name: "EmbeddingIntentClassifier"
  batch_strategy: sequence

Multiple Intents

If you want to split intents into multiple labels, e.g. for predicting multiple intents or for modeling hierarchical intent structure, you can only do this with the supervised embeddings pipeline.

Understanding the Rasa NLU Pipeline

In Rasa NLU, incoming messages are processed by a sequence of components. These components are executed one after another in a so-called processing pipeline. There are components for entity extraction, for intent classification, response selection, pre-processing, and others.

Component Lifecycle

Every component can implement several methods from the Component base class; in a pipeline these different methods will be called in a specific order.

The “entity” object explained

After parsing, the entity is returned as a dictionary. There are two fields that show information about how the pipeline impacted the entities returned: the extractor field of an entity tells you which entity extractor found this particular entity.

Pre-configured Pipelines

A template is just a shortcut for a full list of components.

supervised_embeddings

To train a Rasa model in your preferred language, define the supervised_embeddings pipeline as your pipeline in your config.yml or other configuration file:

language: "en"

pipeline: "supervised_embeddings"

pretrained_embeddings_convert

To use the pretrained_embeddings_convert template:

language: "en"

pipeline: "pretrained_embeddings_convert"

pretrained_embeddings_spacy

To use the pretrained_embeddings_spacy template:

language: "en"

pipeline: "pretrained_embeddings_spacy"

MITIE

To use the MITIE pipeline, you will have to train word vectors from a corpus.

Custom pipelines

You don’t have to use a template, you can also run a fully custom pipeline by listing the names of the components you want to use:

pipeline:
- name: "SpacyNLP"
- name: "CRFEntityExtractor"
- name: "EntitySynonymMapper"